Artificial intelligence, including machine learning, has emerged as a transformational science and engineering discipline. Artificial Intelligence: Foundations of Computational Agents presents AI using a coherent framework to study the design of intelligent computational agents. By showing how the basic approaches fit into a multidimensional design space, readers learn the fundamentals without losing sight of the bigger picture. The new edition also features expanded coverage on machine learning material, as well as on the social and ethical consequences of AI and ML. The book balances theory and experiment, showing how to link them together, and develops the science of AI together with its engineering applications. Although structured as an undergraduate and graduate textbook, the book's straightforward, self-contained style will also appeal to an audience of professionals, researchers, and independent learners. The second edition is well-supported by strong pedagogical features and online resources to enhance student comprehension.

Reviews & endorsements

'This new edition of the already classic Introduction to Artificial Intelligence by Poole and Mackworth provides a broad coverage of the symbolic and non-symbolic approaches underpinning the main current and future approaches to AI. It combines an accessible treatment of the underlying theory with practical examples that bring the theory to life. It is essential reading for anyone who wants to understand the current state of the art, and to be prepared for the future.'
Robert Kowalski, Imperial College London

'Poole and Mackworth provide a crystal clear introduction to artificial intelligence. Their book paints a complete picture of the field, from the logical foundations to the latest breakthroughs in learning, representation, reasoning, and multi-agent systems. The authors view AI as the integration of a diverse set of technologies. Layer by layer, they introduce all the techniques required to build an intelligent agent. The book stands out as being both comprehensive and uncompromising, limiting the material to the most promising and intellectually gratifying topics.'
Guy Van den Broeck, University of California, Los Angeles

Customer reviews

22nd Mar 2018 by Napoleonboakye1

Wow, I love this book. I read the 2010 version online and immediately recommended it to my students and scholars who were taking Ng's ML course on Coursera. It widens your horizon in terms of intelligence. It has been broken down to a layman's understanding and a researcher's intuitiveness. The way artificial intelligence and natural intelligence was explained was best to none. This newer version comes with brevity and conciseness. A must read even if you are only interested in ML.

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Product details

Edition: 2nd Edition

Date Published: November 2017

format: Hardback

isbn: 9781107195394

length: 820pages

dimensions: 261 x 182 x 39 mm

weight: 1.82kg

availability: In stock

Table of Contents

Part I. Agents in the World: What Are Agents and How Can They Be Built?:1. Artificial intelligence and agents 2. Agent architectures and hierarchical control Part II. Reasoning, Planning and Learning with Certainty:3. Searching for solutions 4. Reasoning with constraints 5. Propositions and inference 6. Planning with certainty 7. Supervised machine learning Part III. Reasoning, Learning and Acting with Uncertainty:8. Reasoning with uncertainty 9. Planning with uncertainty 10. Learning with uncertainty 11. Multiagent systems 12. Learning to act Part IV. Reasoning, Learning and Acting with Individuals and Relations:13. Individuals and relations 14. Ontologies and knowledge-based systems 15. Relational planning, learning, and probabilistic reasoning Part V. Retrospect and Prospect:16. Retrospect and prospect Part VI. End Matter: Appendix A. Mathematical preliminaries and notation.

David L. Poole, Alan K. Mackworth

General Resources

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Authors

David L. Poole, University of British Columbia, VancouverDavid L. Poole is a Professor of Computer Science at the University of British Columbia. He is a co-author of three artificial intelligence books including Statistical Relational Artificial Intelligence: Logic, Probability, and Computation (2016). He is a former Chair of the Association for Uncertainty in Artificial Intelligence, the winner of the Canadian AI Association (CAIAC) 2013 Lifetime Achievement Award, and a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and CAIAC.

Alan K. Mackworth, University of British Columbia, VancouverAlan K. Mackworth is a Professor of Computer Science at the University of British Columbia. He has authored over 130 papers and co-authored two books: Computational Intelligence: A Logical Approach (1997) and Artificial Intelligence: Foundations of Computational Agents (2010). His awards include the Artificial Intelligence Journal (AIJ) Classic Paper Award and the Association of Constraint Programming (ACP) Award for Research Excellence. He has served as President of the International Joint Conference on Artificial Intelligence (IJCAI), the Association for the Advancement of Artificial Intelligence (AAAI) and the Canadian AI Association (CAIAC). He is a Fellow of AAAI, CAIAC and the Royal Society of Canada.

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